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A hands-on guide for professionals to perform various data science tasks in R Key Features Explore the popular R packages for data science Use R for efficient data mining, text analytics and feature engineering Become a thorough data science professional with the help of hands-on examples and use-cases in R Book Description R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity. What you will learn Understand the R programming language and its ecosystem of packages for data science Obtain and clean your data before processing Master essential exploratory techniques for summarizing data Examine various machine learning prediction, models Explore the H2O analytics platform in R for deep learning Apply data mining techniques to available datasets Work with interactive visualization packages in R Integrate R with Spark and Hadoop for large-scale data analytics Who this book is for If you are a budding data scientist keen to learn about the popular pandas library, or a Python developer looking to step into the world of data analysis, this book is the ideal resource you need to get started. Some programming experience in Python will be helpful to get the most out of this course

Software. --- Data Science.

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Build the foundational data science skills necessary to work with and better understand complex data science algorithms. This example-driven book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms. The book is self-contained. All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn’t required because complete examples are provided and explained. Data Science Fundamentals with Python and MongoDB is an excellent starting point for those interested in pursuing a career in data science. Like any science, the fundamentals of data science are a prerequisite to competency. Without proficiency in mathematics, statistics, data manipulation, and coding, the path to success is “rocky” at best. The coding examples in this book are concise, accurate, and complete, and perfectly complement the data science concepts introduced. What You'll Learn: Prepare for a career in data science Work with complex data structures in Python Simulate with Monte Carlo and Stochastic algorithms Apply linear algebra using vectors and matrices Utilize complex algorithms such as gradient descent and principal component analysis Wrangle, cleanse, visualize, and problem solve with data Use MongoDB and JSON to work with data.

Programming --- Information systems --- Python (informatica) --- machine learning --- data science --- gegevensanalyse

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This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. The topics cover an extremely wide spectrum of essential and relevant aspects of data science, spanning its evolution, concepts, thinking, challenges, discipline, and foundation, all the way to industrialization, profession, education, and the vast array of opportunities that data science offers. The book's three parts each detail layers of these different aspects. The book is intended for decision-makers, data managers (e.g., analytics portfolio managers, business analytics managers, chief data analytics officers, chief data scientists, and chief data officers), policy makers, management and decision strategists, research leaders, and educators who are responsible for pursuing new scientific, innovation, and industrial transformation agendas, enterprise strategic planning, a next-generation profession-oriented course development, as well as those who are involved in data science, technology, and economy from an advanced perspective. Research students in data science-related courses and disciplines will find the book useful for positing their innovative scientific journey, planning their unique and promising career, and competing within and being ready for the next generation of science, technology, and economy.

Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- datamining --- data science --- gegevensanalyse --- KI (kunstmatige intelligentie) --- data acquisition

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Long description: Einführung in die grundlegenden Konzepte von Machine Learning und Deep LearningZahlreiche praktische Anwendungsbeispiele zum Lösen konkreter AufgabenstellungenMaschinelles Sehen, Sprachverarbeitung, Bildklassifizierung, Vorhersage von Zeitreihen, Stimmungsanalyse, Erzeugung von Bildern und Texten u.v.m.Dieses Buch ist eine praxisorientierte Einführung und erläutert die grundlegenden Konzepte sowie den konkreten Einsatz von Deep Learning. Der Autor verzichtet dabei weitgehend auf mathematische Formeln und legt stattdessen den Fokus auf das Vermitteln der praktischen Anwendung von Machine Learning und Deep Learning.Anhand zahlreicher Beispiele erfahren Sie alles, was Sie wissen müssen, um Deep Learning zum Lösen konkreter Aufgabenstellungen einzusetzen. Dafür verwendet der Autor die Programmiersprache Python und die Deep-Learning-Bibliothek Keras, die das beliebteste und am besten geeignete Tool für den Einstieg in Deep Learning ist.Das Buch besteht aus zwei Teilen: Teil I ist eine allgemeine Einführung in das Deep Learning und erläutert die grundlegenden Zusammenhänge und Begriffe sowie alle erforderlichen Konzepte, die für den Einstieg in Deep Learning und Neuronale Netze wichtig sind. In Teil II erläutert der Autor ausführlich praktische Anwendungsmöglichkeiten des Deep Learnings beim maschinellen Sehen (Computer Vision) und bei der Verarbeitung natürlicher Sprache. Viele der hier vorgestellten Beispiele können Ihnen später als Vorlage zum Lösen von Problemen dienen, die Ihnen in der Praxis des Deep Learnings begegnen werden.Das Buch wendet sich an Leser, die bereits Programmiererfahrung mit Python haben und ins Machine Learning und Deep Learning einsteigen möchten. Für den Einsatz von Keras werden grundlegende Python-Kenntnisse vorausgesetzt. Biographical note: François Chollet ist bei Google tätig und befasst sich mit Deep Learning. Er ist der Entwickler der Deep-Learning-Bibliothek Keras und hat bedeutende Beiträge zum Machine-Learning-Framework TensorFlow geleistet. Er forscht auf dem Gebiet des Deep Learnings mit den Schwerpunkten maschinelles Sehen und der Anwendung des Machine Learnings auf formales Schließen. Seine Forschungsergebnisse wurden auf bedeutenden Veranstaltungen des Fachgebiets veröffentlicht, unter anderem auf der (CVPR), der (NIPS), der (ICLR) und weiteren.

Big Data --- Programmierung --- Künstliche Intelligenz --- Neuronale Netze --- Machine Learning --- Computer Vision --- Data Science --- Keras --- Python-Bibliothek

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Chapter 1. Exploring the Analytics Frontiers through Research and Pedagogy Amit V. Deokar, Ashish Gupta, Lakshmi Iyer, and Mary C. Jones  Chapter 2. Introduction: Research and Research-in-Progress Anna Sidorova, Babita Gupta, and Barbara Dinter  Chapter 3. Business Intelligence Capabilities Thiagarajan Ramakrishnan, Jiban Khuntia, Terence Saldanha, and Abhishek Kathuria  Chapter 4.Big Data Capabilities: An Organizational Information Processing Perspective ÖyküIsik  Chapter 5. Business Analytics Capabilities and Use: A Value Chain Perspective Rudolph T. Bedeley, TorupallabGhoshal, Lakshmi S. Iyer, and JoyenduBhadury  Chapter 6.Critical Value Factors in Business Intelligence Systems Implementations Paul P. Dooley, Yair Levy, Raymond A. Hackney, and James L. Parrish  Chapter 7. Business Intelligence Systems Use in Chinese Organizations Yutong Song, David Arnott, and ShijiaGao  Chapter 8. The Impact of Customer Reviews on Product Innovation: Empirical Evidence in Mobile Apps Zhilei Qiao, G. Alan Wang, Mi Zhou, and Weiguo Fan  Chapter 9. Whispering on Social Media Juheng Zhang  Chapter 10. Does Social Media Reflect Metropolitan Attractiveness? Behavioral Information from Twitter Activity in Urban Areas Johannes Bendler, Tobias Brandt, and Dirk Neumann  Chapter 11. The Competitive Landscape of Mobile Communications Industry in Canada – Predictive Analytic Modeling with Google Trends and Twitter Michal Szczech and OzgurTuretken  Chapter 12. Scale Development Using Twitter Data: Applying Contemporary Natural Language Processing Methods in IS Research David Agogo and Traci J. Hess  Chapter 13. Information Privacy on Online Social Networks: Illusion-in-Progress in the Age of Big Data? Shwadhin Sharma and Babita Gupta Chapter 14. Online Information Processing of Scent-Related Words and Implications for Decision Making Meng-Hsien (Jenny) Lin, Samantha N.N. Cross, William J. Jones, and Terry L. Childers  Chapter 15. Say It Right: IS Prototype to Enable Evidence-Based Communication Using Big Data Simon Alfano  Chapter 16. Introduction: Pedagogy in Analytics and Data Science Nicholas Evangelopoulos, Joseph W. Clark, and Sule Balkan  Chapter 17. Tools for Academic Business Intelligence & Analytics Teaching – Results of an Evaluation Christoph Kollwitz, Barbara Dinter, and Robert Krawatzeck  Chapter 18. Neural Net Tutorial Brian R. Huguenard, and Deborah J. Ballou  Chapter 19. An Examination of ERP Learning Outcomes: A Text Mining Approach Mary M. Dunaway  Chapter 20. Data Science for All: A University-Wide Course in Data Literacy David Schuff.

Programming --- Information systems --- Computer. Automation --- MIS (management informatie systeem) --- bedrijfseconomie --- big data --- mobiele netwerken --- data science --- informatiesystemen --- gegevensanalyse --- informatica management